Minecraft diamond challenge leaves AI creators stumped

>Seriously, I'm starting to think turn based strategy games like Civilization, etc, will be the last to get any attention. Why?

I don't think the answer is technical. A good AI for a game like Civilization would have knowledge of human social concepts like honor and vindictiveness, which are both concepts problematic to pin down. The definitions of concepts like these are fluid, they change with the times and societies in question. But that's not to say any possible setting for these concepts is equally valid when trying to emulate human behavior. Some settings will seem unrealistic, like a mustache twirling villain or a hyper-rational pointy-eared alien. Neither make for good AIs if you're shooting for human-like behavior.

Humans can certainly individually craft fictional personalities that seem realistic; authors do it all the time. But from a gameplay perspective that tactic falls flat; you end up with a limited set of personalities the player becomes familiar with and the game consequently loses relay value. In the Civilization games, every player knows that Gandhi has a short temper and likes to launch nukes. Rather than crafting individual personalities, how does a game designer define a function that returns personalities with a realistic distribution? How can a game designer define such a function if the parameters aren't truly known by science and the artists who craft individual personalities are going off wishy-washy metrics like gut instinct and artistic intuition?

> A human can learn something through a fairly small amount of specific knowledge via written, verbal, etc instruction.

This demands the mastery of language which is a very high bar. Most animals haven't achieved this, so I think if you expect to get a computer to learn this way before being able to reason as well before you consider it having intelligence, you would also have consider most animals not having intelligence as well.

That's my thinking as well. It's much more interesting to see how the AI copes with the world, rather than getting bogged down in OCR and other previously-solved problems.

> Title is misleading. Traditional AI with subroutines for each subtask would have no problem with this.

The easiest way to code this with traditional methods would be to still use deep learning for the image recognition part of it. The input to the agent at each step includes an array of numbers representing the pixels on the screen. So it doesn't get to see the 3d terrain directly; it sees a projection into 2d, and needs to recognize the 3d terrain and objects. And from there it needs to synthesize that into a map of the situation-- for example, the presence of lava, water, cliffs, and hostile monsters.

Doing that kind of object recognition without deep learning would be pretty onerous. The strategy and planning parts of this, though, I agree would be comparatively straightforward. Further, even if it started with a representation of the actual terrain blocks in a 3d model, the synthesis step into a representation of what the overall situation is-- that also is much more easily done with deep learning than with traditional methods.

Back in the day I wrote a highly effective bot for playing Star Wars Galaxies which would run missions unattended. It was created exclusively with hand-coded routines and a big nested state machine. It included a hand-written OCR library for reading the compass coordinates and accepting missions from the mission terminal in a particular direction. It knew a pre-recorded path for navigating in and out of town to and from the mission terminal. It used the little red dots on the radar scanner to determine the presence of nearby hostile creatures and mission targets. I ended up selling this system to a gold farming outfit in China; it was only available for sale to the public for a handful of days.

I bring this up because this bot for SWG didn't contain any modern machine learning or deep learning techniques, and it worked great. But if it weren't for the coordinates displayed on the radar (used for navigating through the maze of town back to the mission terminal), along with easily discernible red dots on the radar indicating enemy presence (used for knowing which direction to face during combat), I'd have been at a loss for the complex object recognition / situation recognition necessary to turn this into a task solvable by a straightforward nested state machine.

I'm very curious why you're so skeptical of this. The AI that was created for the Civilization games did in fact try to model these and similar aspects of human behavior, with the intent of creating AIs that played in ways humans would consider natural or fitting. They named the AIs after historical humans and tried to make the AIs act in ways that would be perceived as similar to their namesakes. Nuke-happy Gandhi was originally a bug; that AI was intended to be a pacifist but apparently the in-game invention of democracy causes that value to underflow, rendering him as maximally aggressive. This trait was kept in later games due to fan demand, but that's the exception to the rule. Generally these AIs are intended to behave as humans might behave.

These games have parameters for loyalty, forgiveness, propensity towards warmongering, and more. That's part of what these games are. If you took away that philosophy towards AI design, you'd no longer have a Civilization game.